code: def get_meanshift(X, labels, n_clusters, **kwargs):
      bandwidth = estimate_bandwidth(X, quantile=0.25)
      model = MeanShift(bandwidth=bandwidth, bin_seeding=True)
      model.set_params(**kwargs)
      return model.fit(X)
  
imports: import gradio
  import math
  from functools import partial
  import matplotlib.pyplot
  import numpy
  from sklearn.cluster import AgglomerativeClustering, Birch, DBSCAN, KMeans, MeanShift, OPTICS, SpectralClustering, estimate_bandwidth
  from sklearn.datasets import make_blobs, make_circles, make_moons
  from sklearn.mixture import GaussianMixture
  from sklearn.neighbors import kneighbors_graph
  from sklearn.preprocessing import StandardScaler
label_desc: Use image node_id 13 for calling a global function with example usage: get_meanshift(X, labels, n_clusters) and returns: model